Abstract
The auto-management of vehicle entrance and parking in any organization is a complex challenge encompassing record-keeping, efficiency, and security concerns. Manual methods for tracking vehicles and finding parking spaces are slow and a waste of time. To solve the problem of auto management of vehicle entrance and parking, we have utilized state-of-the-art deep learning models and automated the process of vehicle entrance and parking into any organization. To ensure security, our system integrated vehicle detection, license number plate verification, and face detection and recognition models to ensure that the person and vehicle are registered with the organization. We have trained multiple deep-learning models for vehicle detection, license number plate detection, face detection, and recognition, however, the YOLOv8n model outperformed all the other models. Furthermore, License plate recognition is facilitated by Google's Tesseract-OCR Engine. By integrating these technologies, the system offers efficient vehicle detection, precise identification, streamlined record keeping, and optimized parking slot allocation in buildings, thereby enhancing convenience, accuracy, and security. Future research opportunities lie in fine-tuning system performance for a wide range of real-world applications.
Abstract (translated)
任何组织对车辆入口和停车的自动管理是一个复杂而广泛的挑战，涉及记录、效率和安全问题。手动跟踪车辆和查找停车位的方法缓慢而且浪费时间。为解决车辆入口和停车的自动管理问题，我们利用了最先进的人工智能技术，将车辆入口和停车的过程自动化到任何组织中。为了确保安全性，我们的系统集成了车辆检测、车牌识别和人脸识别模型，以确保人和车辆与组织注册。我们已经为车辆检测、车牌识别、人脸识别和识别训练了多个深度学习模型，然而，YOLOv8n模型在其他模型中表现出色。此外，通过整合这些技术，系统提供了高效的车辆检测、精确的身份识别、简洁的记录和优化的停车位分配，从而提高了便利性、准确性和安全性。未来研究机会在于对各种现实应用进行系统性能的微调。
URL
https://arxiv.org/abs/2312.02699
